# Transformer-XL and XLNet

Transformer-XL and XLNet are advanced transformer-based models that have made significant contributions to natural language processing (NLP) tasks. In this guide, we will explore each model in detail, providing factual information and detailed examples to illustrate their capabilities.

1. Transformer-XL:

Transformer-XL is an extension of the original transformer model that addresses the limitation of sequence length during training. It introduces a segment-level recurrence mechanism that enables capturing long-term dependencies in sequential data.

Example: To illustrate the usage of Transformer-XL, let's consider a language modeling task. Given a partial sentence, the goal is to predict the next word in the sequence.

Input: "I have a cat and a"

Output: "dog"

By training Transformer-XL on a large corpus of text data, it can learn the language patterns and probabilities to generate accurate predictions for the next word in a sequence.

2. XLNet:

XLNet is a generalized autoregressive pretraining method that overcomes the limitations of previous models by considering all possible permutations of the input data. It utilizes a permutation-based training objective to capture bidirectional context without the need for masked language modeling.

Example: To demonstrate the usage of XLNet, let's consider a text classification task. The objective is to classify movie reviews as positive or negative based on their sentiment.

Input Text: "This movie is the worst I have ever seen. The acting was terrible, and the plot was nonsensical."

Sentiment Output: "Negative"

By fine-tuning XLNet on a sentiment analysis dataset with labeled movie reviews, it can learn to classify new reviews as positive or negative based on the sentiment expressed in the text.

Detailed Examples:

1. Transformer-XL:

Consider an example of text completion using Transformer-XL. The task is to generate coherent and contextually relevant completions for given partial sentences.

Input Text: "Once upon a time, in a land far,"

Completion Output: "far away, there was a kingdom ruled by a wise and just king. The kingdom was known for its lush green fields and sparkling rivers."

By training Transformer-XL on a dataset containing partial sentences and their completions, it can learn to generate meaningful and engaging completions for new partial sentences.

2. XLNet:

Let's consider an example of named entity recognition (NER) using XLNet. The goal is to identify and classify specific entities in a given text.

Input Text: "Apple Inc. is planning to launch a new product next year."

NER Output: {"entities": \[{"text": "Apple Inc.", "start": 0, "end": 9, "label": "ORG"}]}

By fine-tuning XLNet on an NER dataset with labeled entities, we can train it to accurately identify and classify entities like organization names (ORG) in new texts.

In conclusion, Transformer-XL and XLNet are advanced transformer-based models that have made significant contributions to NLP tasks. Transformer-XL introduces a segment-level recurrence mechanism to capture long-term dependencies, while XLNet utilizes a permutation-based training objective to capture bidirectional context. These models can be fine-tuned for tasks such as text completion, sentiment analysis, named entity recognition, and more, achieving state-of-the-art results.
